A Combinatorial Approach to Detecting Gene-Gene and Gene-Environment Interactions in Family Studies

Lou, Xiang-Yang, Chen, Guo-Bo, Yan, Lei, Ma, Jennie Z., Mangold, Jamie E., Zhu, Jun, Elston, Robert C. and Li, Ming D (2008) A Combinatorial Approach to Detecting Gene-Gene and Gene-Environment Interactions in Family Studies. The American Journal of Human Genetics, 83 4: 457-467. doi:10.1016/j.ajhg.2008.09.001


Author Lou, Xiang-Yang
Chen, Guo-Bo
Yan, Lei
Ma, Jennie Z.
Mangold, Jamie E.
Zhu, Jun
Elston, Robert C.
Li, Ming D
Title A Combinatorial Approach to Detecting Gene-Gene and Gene-Environment Interactions in Family Studies
Journal name The American Journal of Human Genetics   Check publisher's open access policy
ISSN 0002-9297
1537-6605
Publication date 2008
Year available 2008
Sub-type Article (original research)
DOI 10.1016/j.ajhg.2008.09.001
Open Access Status
Volume 83
Issue 4
Start page 457
End page 467
Total pages 11
Place of publication Cambridge, MA United States
Publisher Cell Press
Collection year 2008
Language eng
Formatted abstract
Widespread multifactor interactions present a significant challenge in determining risk factors of complex diseases. Several combinatorial approaches, such as the multifactor dimensionality reduction (MDR) method, have emerged as a promising tool for better detecting gene-gene (G × G) and gene-environment (G × E) interactions. We recently developed a general combinatorial approach, namely the generalized multifactor dimensionality reduction (GMDR) method, which can entertain both qualitative and quantitative phenotypes and allows for both discrete and continuous covariates to detect G × G and G × E interactions in a sample of unrelated individuals. In this article, we report the development of an algorithm that can be used to study G × G and G × E interactions for family-based designs, called pedigree-based GMDR (PGMDR). Compared to the available method, our proposed method has several major improvements, including allowing for covariate adjustments and being applicable to arbitrary phenotypes, arbitrary pedigree structures, and arbitrary patterns of missing marker genotypes. Our Monte Carlo simulations provide evidence that the PGMDR method is superior in performance to identify epistatic loci compared to the MDR-pedigree disequilibrium test (PDT). Finally, we applied our proposed approach to a genetic data set on tobacco dependence and found a significant interaction between two taste receptor genes (i.e., TAS2R16 and TAS2R38) in affecting nicotine dependence.
Keyword Quasi likelihood functions
Generalized linear models
Nicotine dependence
Quantitative Traits
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ
Additional Notes For ERA

Document type: Journal Article
Sub-type: Article (original research)
Collection: Queensland Brain Institute Publications
 
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Created: Thu, 23 Oct 2014, 10:06:14 EST by Debra McMurtrie on behalf of Queensland Brain Institute